import tensorflow as tf
def gated_linear_layer(inputs, gates, name=None):
activation = tf.multiply(x=inputs, y=tf.sigmoid(gates), name=name)
return activation
def instance_norm_layer(
inputs,
epsilon=1e-06,
activation_fn=None,
name=None):
instance_norm_layer = tf.contrib.layers.instance_norm(
inputs=inputs,
epsilon=epsilon,
activation_fn=activation_fn)
return instance_norm_layer
def conv1d_layer(
inputs,
filters,
kernel_size,
strides=1,
padding='same',
activation=None,
kernel_initializer=None,
name=None):
conv_layer = tf.layers.conv1d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
activation=activation,
kernel_initializer=kernel_initializer,
name=name)
return conv_layer
def conv2d_layer(
inputs,
filters,
kernel_size,
strides,
padding='same',
activation=None,
kernel_initializer=None,
name=None):
conv_layer = tf.layers.conv2d(
inputs=inputs,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
activation=activation,
kernel_initializer=kernel_initializer,
name=name)
return conv_layer
def residual1d_block(
inputs,
filters=1024,
kernel_size=3,
strides=1,
name_prefix='residule_block_'):
h1 = conv1d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_conv')
h1_norm = instance_norm_layer(inputs=h1, activation_fn=None, name=name_prefix + 'h1_norm')
h1_gates = conv1d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_gates')
h1_norm_gates = instance_norm_layer(inputs=h1_gates, activation_fn=None, name=name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_norm_gates, name=name_prefix + 'h1_glu')
h2 = conv1d_layer(inputs=h1_glu, filters=filters // 2, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h2_conv')
h2_norm = instance_norm_layer(inputs=h2, activation_fn=None, name=name_prefix + 'h2_norm')
h3 = inputs + h2_norm
return h3
def downsample1d_block(
inputs,
filters,
kernel_size,
strides,
name_prefix='downsample1d_block_'):
h1 = conv1d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_conv')
h1_norm = instance_norm_layer(inputs=h1, activation_fn=None, name=name_prefix + 'h1_norm')
h1_gates = conv1d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_gates')
h1_norm_gates = instance_norm_layer(inputs=h1_gates, activation_fn=None, name=name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_norm_gates, name=name_prefix + 'h1_glu')
return h1_glu
def downsample2d_block(
inputs,
filters,
kernel_size,
strides,
name_prefix='downsample2d_block_'):
h1 = conv2d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_conv')
h1_norm = instance_norm_layer(inputs=h1, activation_fn=None, name=name_prefix + 'h1_norm')
h1_gates = conv2d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_gates')
h1_norm_gates = instance_norm_layer(inputs=h1_gates, activation_fn=None, name=name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_norm_gates, name=name_prefix + 'h1_glu')
return h1_glu
def upsample1d_block(
inputs,
filters,
kernel_size,
strides,
shuffle_size=2,
name_prefix='upsample1d_block_'):
h1 = conv1d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_conv')
h1_shuffle = pixel_shuffler(inputs=h1, shuffle_size=shuffle_size, name=name_prefix + 'h1_shuffle')
h1_norm = instance_norm_layer(inputs=h1_shuffle, activation_fn=None, name=name_prefix + 'h1_norm')
h1_gates = conv1d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_gates')
h1_shuffle_gates = pixel_shuffler(inputs=h1_gates, shuffle_size=shuffle_size, name=name_prefix + 'h1_shuffle_gates')
h1_norm_gates = instance_norm_layer(inputs=h1_shuffle_gates, activation_fn=None, name=name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_norm_gates, name=name_prefix + 'h1_glu')
return h1_glu
def upsample2d_block(
inputs,
filters,
kernel_size,
strides,
shuffle_size=2,
name_prefix='upsample2d_block_'):
h1 = conv2d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_conv')
h1_shuffle = tf.depth_to_space(input=h1, block_size=2, name='h1_shuffle')
h1_norm = instance_norm_layer(inputs=h1_shuffle, activation_fn=None, name=name_prefix + 'h1_norm')
h1_gates = conv2d_layer(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides, activation=None,
name=name_prefix + 'h1_gates')
h1_shuffle_gates = tf.depth_to_space(input=h1_gates, block_size=2, name='h1_shuffle_gates')
h1_norm_gates = instance_norm_layer(inputs=h1_shuffle_gates, activation_fn=None, name=name_prefix + 'h1_norm_gates')
h1_glu = gated_linear_layer(inputs=h1_norm, gates=h1_norm_gates, name=name_prefix + 'h1_glu')
return h1_glu
def pixel_shuffler(inputs, shuffle_size=2, name=None):
n = tf.shape(inputs)[0]
w = tf.shape(inputs)[1]
c = inputs.get_shape().as_list()[2]
oc = c // shuffle_size
ow = w * shuffle_size
outputs = tf.reshape(tensor=inputs, shape=[n, ow, oc], name=name)
return outputs
def generator_gatedcnn(inputs, dim=24, reuse=False, scope_name='generator_gatedcnn'):
# inputs has shape [batch_size, num_features, time]
# we need to convert it to [batch_size, time, num_features] for 1D convolution
inputs = tf.transpose(inputs, perm=[0, 2, 1], name='input_transpose')
with tf.variable_scope(scope_name) as scope:
# Discriminator would be reused in CycleGAN
if reuse:
scope.reuse_variables()
else:
assert scope.reuse is False
h1 = conv1d_layer(inputs=inputs, filters=128, kernel_size=15, strides=1, activation=None, name='h1_conv')
h1_gates = conv1d_layer(inputs=inputs, filters=128, kernel_size=15, strides=1, activation=None,
name='h1_conv_gates')
h1_glu = gated_linear_layer(inputs=h1, gates=h1_gates, name='h1_glu')
# Downsample
d1 = downsample1d_block(inputs=h1_glu, filters=256, kernel_size=5, strides=2,
name_prefix='downsample1d_block1_')
d2 = downsample1d_block(inputs=d1, filters=512, kernel_size=5, strides=2, name_prefix='downsample1d_block2_')
# Residual blocks
r1 = residual1d_block(inputs=d2, filters=1024, kernel_size=3, strides=1, name_prefix='residual1d_block1_')
r2 = residual1d_block(inputs=r1, filters=1024, kernel_size=3, strides=1, name_prefix='residual1d_block2_')
r3 = residual1d_block(inputs=r2, filters=1024, kernel_size=3, strides=1, name_prefix='residual1d_block3_')
r4 = residual1d_block(inputs=r3, filters=1024, kernel_size=3, strides=1, name_prefix='residual1d_block4_')
r5 = residual1d_block(inputs=r4, filters=1024, kernel_size=3, strides=1, name_prefix='residual1d_block5_')
r6 = residual1d_block(inputs=r5, filters=1024, kernel_size=3, strides=1, name_prefix='residual1d_block6_')
# Upsample
u1 = upsample1d_block(inputs=r6, filters=1024, kernel_size=5, strides=1, shuffle_size=2,
name_prefix='upsample1d_block1_')
u2 = upsample1d_block(inputs=u1, filters=512, kernel_size=5, strides=1, shuffle_size=2,
name_prefix='upsample1d_block2_')
# Output
o1 = conv1d_layer(inputs=u2, filters=dim, kernel_size=15, strides=1, activation=None, name='o1_conv')
o2 = tf.transpose(o1, perm=[0, 2, 1], name='output_transpose')
return o2
def discriminator(inputs, reuse=False, scope_name='discriminator'):
# inputs has shape [batch_size, num_features, time]
# we need to add channel for 2D convolution [batch_size, num_features, time, 1]
inputs = tf.expand_dims(inputs, -1)
with tf.variable_scope(scope_name) as scope:
# Discriminator would be reused in CycleGAN
if reuse:
scope.reuse_variables()
else:
assert scope.reuse is False
h1 = conv2d_layer(inputs=inputs, filters=128, kernel_size=[3, 3], strides=[1, 2], activation=None,
name='h1_conv')
h1_gates = conv2d_layer(inputs=inputs, filters=128, kernel_size=[3, 3], strides=[1, 2], activation=None,
name='h1_conv_gates')
h1_glu = gated_linear_layer(inputs=h1, gates=h1_gates, name='h1_glu')
# Downsample
d1 = downsample2d_block(inputs=h1_glu, filters=256, kernel_size=[3, 3], strides=[2, 2],
name_prefix='downsample2d_block1_')
d2 = downsample2d_block(inputs=d1, filters=512, kernel_size=[3, 3], strides=[2, 2],
name_prefix='downsample2d_block2_')
d3 = downsample2d_block(inputs=d2, filters=1024, kernel_size=[6, 3], strides=[1, 2],
name_prefix='downsample2d_block3_')
# Output
o1 = tf.layers.dense(inputs=d3, units=1, activation=tf.nn.sigmoid)
return o1
def Generator212(inputs, dim=24, batch_size=1, reuse=False, scope_name='generator_212'):
# inputs has shape [batch_size, num_features, time]
# we need to add channel for 2D convolution [batch_size, num_features, time, 1]
inputs = tf.expand_dims(inputs, -1)
with tf.variable_scope(scope_name) as scope:
# Discriminator would be reused in CycleGAN
if reuse:
scope.reuse_variables()
else:
assert scope.reuse is False
h1 = conv2d_layer(inputs=inputs, filters=128, kernel_size=[5, 15], strides=[1, 1], activation=None,
name='h1_conv')
h1_gates = conv2d_layer(inputs=inputs, filters=128, kernel_size=[5, 15], strides=[1, 1], activation=None,
name='h1_conv_gates')
h1_glu = gated_linear_layer(inputs=h1, gates=h1_gates, name='h1_glu')
d1 = downsample2d_block(inputs=h1_glu, filters=256, kernel_size=[5, 5], strides=[2, 2],
name_prefix='downsample2d_block1_')
d2 = downsample2d_block(inputs=d1, filters=256, kernel_size=[5, 5], strides=[2, 2],
name_prefix='downsample2d_block2_')
d3 = tf.squeeze(tf.reshape(d2, shape=(batch_size, 1, -1, 2304)), axis=[1], name='d2_reshape')
resh1 = conv1d_layer(inputs=d3, filters=256, kernel_size=1, strides=1, activation=None, name='resh1_conv')
resh1_norm = instance_norm_layer(inputs=resh1, activation_fn=None, name='resh1_norm')
r1 = residual1d_block(inputs=resh1_norm, filters=512, kernel_size=3, strides=1, name_prefix='res1d_block1_')
r2 = residual1d_block(inputs=r1, filters=512, kernel_size=3, strides=1, name_prefix='res1d_block2_')
r3 = residual1d_block(inputs=r2, filters=512, kernel_size=3, strides=1, name_prefix='res1d_block3_')
r4 = residual1d_block(inputs=r3, filters=512, kernel_size=3, strides=1, name_prefix='res1d_block4_')
r5 = residual1d_block(inputs=r4, filters=512, kernel_size=3, strides=1, name_prefix='res1d_block5_')
r6 = residual1d_block(inputs=r5, filters=512, kernel_size=3, strides=1, name_prefix='res1d_block6_')
resh2 = conv1d_layer(inputs=r6, filters=2304, kernel_size=1, strides=1, activation=None, name='resh2_conv')
resh2_norm = instance_norm_layer(inputs=resh2, activation_fn=None, name='resh2_norm')
resh3 = tf.reshape(tf.expand_dims(resh2_norm, axis=1), shape=(batch_size, 9, -1, 256), name='resh2_reshape')
# Upsample
u1 = upsample2d_block(inputs=resh3, filters=1024, kernel_size=5, strides=1, shuffle_size=2,
name_prefix='upsample2d_block1_')
u2 = upsample2d_block(inputs=u1, filters=512, kernel_size=5, strides=1, shuffle_size=2,
name_prefix='upsample2d_block2_')
conv_out = conv2d_layer(inputs=u2, filters=1, kernel_size=[5, 15], strides=[1, 1], activation=None,
name='conv_out')
out = tf.squeeze(conv_out, axis=[-1], name='out_squeeze')
return out
def PatchGanDiscriminator(inputs, reuse=False, scope_name='discriminator'):
# inputs has shape [batch_size, num_features, time]
# we need to add channel for 2D convolution [batch_size, num_features, time, 1]
inputs = tf.expand_dims(inputs, -1)
with tf.variable_scope(scope_name) as scope:
# Discriminator would be reused in CycleGAN
if reuse:
scope.reuse_variables()
else:
assert scope.reuse is False
h1 = conv2d_layer(inputs=inputs, filters=128, kernel_size=[3, 3], strides=[1, 1], activation=None,
name='h1_conv')
h1_gates = conv2d_layer(inputs=inputs, filters=128, kernel_size=[3, 3], strides=[1, 1], activation=None,
name='h1_conv_gates')
h1_glu = gated_linear_layer(inputs=h1, gates=h1_gates, name='h1_glu')
d1 = downsample2d_block(inputs=h1_glu, filters=256, kernel_size=[3, 3], strides=[2, 2],
name_prefix='downsample2d_block1_')
d2 = downsample2d_block(inputs=d1, filters=512, kernel_size=[3, 3], strides=[2, 2],
name_prefix='downsample2d_block2_')
d3 = downsample2d_block(inputs=d2, filters=1024, kernel_size=[3, 3], strides=[2, 2],
name_prefix='downsample2d_block3_')
d4 = downsample2d_block(inputs=d3, filters=1024, kernel_size=[1, 5], strides=[1, 1],
name_prefix='downsample2d_block4_')
out = conv2d_layer(inputs=d4, filters=1, kernel_size=[1, 3], strides=[1, 1], activation=None,
name='out_conv')
return out
